Purpose

Here we perform a second pass look at the data, similar to the first pass, but focusing specifically on multi-UTR genes.

Initialization

Libraries

library(magrittr)
library(tidyverse)
library(cowplot)
library(plotly)
library(ggbeeswarm)
library(Matrix)
library(matrixStats)
library(SingleCellExperiment)

Parameters

set.seed(20210818)
FILE_SCE_TX="data/sce/kd6_essential.annot.txs.Rds"

Data

Loading

sce <- readRDS(FILE_SCE_TX) %>% `[`(rowData(.)$utr_type_raw == 'multi',)

Preprocessing

Among our statistics, we will compute the percentage of UMIs corresponding to specific isoform classes, including IPA, proximal, and distal, in multi-UTR genes.

df_cells <- colData(sce) %>% as_tibble %>%
    mutate(umis_total=colSums(counts(sce)),
           pct_ipa=colSums(counts(sce[rowData(sce)$is_ipa,]))/umis_total,
           pct_proximal=colSums(counts(sce[rowData(sce)$is_proximal,]))/umis_total,
           pct_distal=colSums(counts(sce[rowData(sce)$is_distal,]))/umis_total)

Target Analysis

We will explore whether any perturbations have effects on basic transcriptome characteristics. These will also be compared against each other.

df_targets <- df_cells %>%
    group_by(target_gene, target_gene_id, sgID_AB) %>%
    summarize(n_cells=dplyr::n(),
              mean_umis_total=mean(umis_total),
              pct_ipa=weighted.mean(pct_ipa, umis_total),
              pct_proximal=weighted.mean(pct_proximal, umis_total),
              pct_distal=weighted.mean(pct_distal, umis_total), .groups='drop')

IPA

df_targets %>%
    mutate(sgID_AB=fct_reorder(sgID_AB, -pct_ipa)) %>%
    ggplot(aes(x=sgID_AB, y=pct_ipa, fill=target_gene == "non-targeting")) +
    geom_bar(stat='identity') +
    scale_y_continuous(expand=c(0,0,0.05,0)) +
    scale_fill_manual(values=c('grey', 'red')) +
    theme_bw() +
    theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) + 
    labs(x="Target Gene", y="Percent UMIs IPA", fill="Control")

g <- df_targets %>%
    mutate(is_nt=target_gene == "non-targeting") %>%
    ggplot(aes(x="Perturbations", y=pct_ipa)) +
    geom_violin(fill='lightgrey', size=0.2, draw_quantiles=c(0.25, 0.50, 0.75)) +
    geom_quasirandom(aes(color=is_nt, size=is_nt, text=target_gene), 
                     pch=16) +
    scale_color_manual(values=c("black", "red")) +
    scale_size_manual(values=c(0.1,1), guide='none') +
    scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
    theme_bw() +
    labs(x=NULL, y="Percent UMIs from IPA isoforms",
         color="Control")
## Warning: Ignoring unknown aesthetics: text
ggplotly(g, tooltip=c("text", "y")) %>%
    style(hoverinfo = "skip", traces = 1)
df_targets %>%
    select(target_gene, pct_ipa, mean_umis_total, sgID_AB) %>%
    slice_max(pct_ipa, n=20)
## # A tibble: 20 × 4
##    target_gene pct_ipa mean_umis_total sgID_AB                                  
##    <chr>         <dbl>           <dbl> <chr>                                    
##  1 AARS          0.197           8131. AARS_+_70323362.23-P1P2|AARS_-_70323332.…
##  2 SNRPD2        0.196           8254. SNRPD2_+_46195119.23-P1P2|SNRPD2_+_46195…
##  3 HSPA9         0.196           7158. HSPA9_-_137911079.23-P1P2|HSPA9_-_137911…
##  4 CHMP3         0.195           9972. CHMP3_-_86790522.23-P1P2|CHMP3_-_8679045…
##  5 SARS          0.195           7045. SARS_+_109756585.23-P1P2|SARS_-_10975660…
##  6 TIMM23B       0.193           7987. TIMM23B_-_51371488.23-P1P2|TIMM23B_-_513…
##  7 PHB           0.193           7139. PHB_+_47492183.23-P1P2|PHB_+_47492231.23…
##  8 QARS          0.193           7764. QARS_-_49142149.23-P1P2|QARS_-_49142355.…
##  9 RTF1          0.193           7844. RTF1_-_41709376.23-P1P2|RTF1_-_41709401.…
## 10 PHB2          0.193           7284. PHB2_+_7079788.23-P1P2|PHB2_-_7079800.23…
## 11 EIF3D         0.192           8899. EIF3D_+_36925166.23-P1P2|EIF3D_-_3692515…
## 12 EIF2S1        0.192           7588. EIF2S1_-_67827085.23-P1P2|EIF2S1_-_67827…
## 13 LSM6          0.192          10412. LSM6_-_147096941.23-P1P2|LSM6_-_14709691…
## 14 SUPT16H       0.192           7730. SUPT16H_-_21851767.23-P1P2|SUPT16H_-_218…
## 15 SNRPD1        0.192           8701. SNRPD1_+_19192365.23-P1P2|SNRPD1_+_19192…
## 16 MED30         0.192           4428. MED30_+_118533216.23-P1P2|MED30_-_118533…
## 17 RBM22         0.192          10107. RBM22_-_150080586.23-P1P2|RBM22_-_150080…
## 18 EIF2B4        0.192           8104. EIF2B4_+_27593168.23-P1P2|EIF2B4_+_27593…
## 19 HARS          0.192           7389. HARS_+_140070923.23-P1P2|HARS_+_14007091…
## 20 PAF1          0.192           6175. PAF1_+_39881751.23-P1P2|PAF1_-_39881702.…
df_targets %>%
    select(target_gene, pct_ipa, mean_umis_total, sgID_AB) %>%
    slice_min(pct_ipa, n=20)
## # A tibble: 20 × 4
##    target_gene pct_ipa mean_umis_total sgID_AB                                  
##    <chr>         <dbl>           <dbl> <chr>                                    
##  1 TAF6          0.174           7708. TAF6_-_99716931.23-P1P2|TAF6_+_99716935.…
##  2 CSTF3         0.174           8171. CSTF3_+_33183038.23-P1P2|CSTF3_-_3318280…
##  3 KRI1          0.175           9428. KRI1_+_10676632.23-P1P2|KRI1_-_10676612.…
##  4 RPS3          0.175           9594. RPS3_-_75110633.23-P1|RPS3_-_75110586.23…
##  5 NOP14         0.175           8507. NOP14_+_2965021.23-P1P2|NOP14_+_2965104.…
##  6 RPS4X         0.175           9604. RPS4X_+_71497070.23-P1P2|RPS4X_-_7149704…
##  7 UTP3          0.175           8015. UTP3_+_71554274.23-P1P2|UTP3_-_71554284.…
##  8 POP1          0.175           9301. POP1_-_99129573.23-P1P2|POP1_-_99129576.…
##  9 RPS19BP1      0.175           8611. RPS19BP1_-_39928789.23-P1P2|RPS19BP1_-_3…
## 10 RIOK2         0.175           9175. RIOK2_+_96518912.23-P1P2|RIOK2_+_9651890…
## 11 PPRC1         0.176           8350. PPRC1_-_103892809.23-P1P2|PPRC1_-_103892…
## 12 RRP12         0.176           8715. RRP12_+_99161057.23-P1P2|RRP12_-_9916103…
## 13 NOP58         0.176           8749. NOP58_-_203130504.23-P1P2|NOP58_-_203130…
## 14 DIMT1         0.176           8641. DIMT1_+_61699702.23-P1P2|DIMT1_+_6169966…
## 15 NEPRO         0.176           9597. C3orf17_-_112738463.23-P1P2|C3orf17_+_11…
## 16 CPSF4         0.176           6830. CPSF4_-_99036611.23-P1P2|CPSF4_-_9903657…
## 17 RPS24         0.176           9498. RPS24_+_79793627.23-P1P2|RPS24_+_7979369…
## 18 PDCD11        0.176           9118. PDCD11_-_105156445.23-P1P2|PDCD11_+_1051…
## 19 RPP40         0.176           8805. RPP40_-_5004221.23-P1P2|RPP40_+_5004190.…
## 20 UTP6          0.176           9415. UTP6_-_30228710.23-P1P2|UTP6_-_30228722.…

Proximal

df_targets %>%
    mutate(sgID_AB=fct_reorder(sgID_AB, -pct_proximal)) %>%
    ggplot(aes(x=sgID_AB, y=pct_proximal, fill=target_gene == "non-targeting")) +
    geom_bar(stat='identity') +
    scale_y_continuous(expand=c(0,0,0.05,0)) +
    scale_fill_manual(values=c('grey', 'red')) +
    theme_bw() +
    theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) + 
    labs(x="Target Gene", y="Percent UMIs Proximal Transcript", fill="Control")

g <- df_targets %>%
    mutate(is_nt=target_gene == "non-targeting") %>%
    ggplot(aes(x="Perturbations", y=pct_proximal)) +
    geom_violin(fill='lightgrey', size=0.2, draw_quantiles=c(0.25, 0.50, 0.75)) +
    geom_quasirandom(aes(color=is_nt, size=is_nt, text=target_gene), 
                     pch=16) +
    scale_color_manual(values=c("black", "red")) +
    scale_size_manual(values=c(0.1,1), guide='none') +
    scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
    theme_bw() +
    labs(x=NULL, y="Percent UMIs from proximal isoforms",
         color="Control")
## Warning: Ignoring unknown aesthetics: text
ggplotly(g, tooltip=c("text", "y")) %>%
    style(hoverinfo = "skip", traces = 1)
df_targets %>%
    select(target_gene, pct_proximal, mean_umis_total, sgID_AB) %>%
    slice_max(pct_proximal, n=20)
## # A tibble: 20 × 4
##    target_gene pct_proximal mean_umis_total sgID_AB                             
##    <chr>              <dbl>           <dbl> <chr>                               
##  1 NUDT21            0.102            9659. NUDT21_+_56485197.23-P1P2|NUDT21_-_…
##  2 CPSF6             0.100            8542. CPSF6_+_69633592.23-P1P2|CPSF6_+_69…
##  3 OGFOD1            0.0996          10061. OGFOD1_-_56485723.23-P1P2|OGFOD1_+_…
##  4 RPS8              0.0945           7730. RPS8_+_45241261.23-P1P2|RPS8_-_4524…
##  5 RPS4X             0.0938           9604. RPS4X_+_71497070.23-P1P2|RPS4X_-_71…
##  6 RPS13             0.0936           8821. RPS13_+_17099232.23-P1P2|RPS13_+_17…
##  7 RPS6              0.0936           8773. RPS6_+_19380221.23-P1P2|RPS6_+_1938…
##  8 PDCD11            0.0936           9118. PDCD11_-_105156445.23-P1P2|PDCD11_+…
##  9 RPS16             0.0936           9434. RPS16_+_39926539.23-P1P2|RPS16_-_39…
## 10 UTP6              0.0934           9415. UTP6_-_30228710.23-P1P2|UTP6_-_3022…
## 11 RPS3              0.0934           9594. RPS3_-_75110633.23-P1|RPS3_-_751105…
## 12 PPRC1             0.0933           8350. PPRC1_-_103892809.23-P1P2|PPRC1_-_1…
## 13 DDX47             0.0932           9380. DDX47_+_12966332.23-P1P2|DDX47_+_12…
## 14 RPS19             0.0932           8936. RPS19_-_42364369.23-P1P2|RPS19_+_42…
## 15 BMS1              0.0931           9359. BMS1_+_43278253.23-P1P2|BMS1_-_4327…
## 16 NOL6              0.0930           9283. NOL6_-_33473848.23-P1P2|NOL6_+_3347…
## 17 CMTR1             0.0930           8350. CMTR1_-_37401025.23-P1P2|CMTR1_-_37…
## 18 RPS24             0.0930           9498. RPS24_+_79793627.23-P1P2|RPS24_+_79…
## 19 RPS27A            0.0928          10186. RPS27A_-_55459862.23-P1P2|RPS27A_+_…
## 20 RPS18             0.0928           8946. RPS18_+_33239917.23-P1P2|RPS18_+_33…
df_targets %>%
    select(target_gene, pct_proximal, mean_umis_total, sgID_AB) %>%
    slice_min(pct_proximal, n=20)
## # A tibble: 20 × 4
##    target_gene pct_proximal mean_umis_total sgID_AB                             
##    <chr>              <dbl>           <dbl> <chr>                               
##  1 RTF1              0.0746           7844. RTF1_-_41709376.23-P1P2|RTF1_-_4170…
##  2 RBM25             0.0779           8632. RBM25_+_73525283.23-P1P2|RBM25_-_73…
##  3 RBM22             0.0785          10107. RBM22_-_150080586.23-P1P2|RBM22_-_1…
##  4 CTR9              0.0786           6545. CTR9_+_10772865.23-P1P2|CTR9_+_1077…
##  5 PAF1              0.0790           6175. PAF1_+_39881751.23-P1P2|PAF1_-_3988…
##  6 NPAT              0.0795          11484. NPAT_-_108093360.23-P1P2|NPAT_+_108…
##  7 POT1              0.0798           8985. POT1_+_124569865.23-P1P2|POT1_-_124…
##  8 SMN2              0.0806           8704. SMN2_-_69345531.23-ENST00000380743.…
##  9 SFPQ              0.0808           7237. SFPQ_-_35658705.23-P1P2|SFPQ_-_3565…
## 10 SUPT16H           0.0808           7730. SUPT16H_-_21851767.23-P1P2|SUPT16H_…
## 11 SARS              0.0813           7045. SARS_+_109756585.23-P1P2|SARS_-_109…
## 12 SNRPD2            0.0814           8254. SNRPD2_+_46195119.23-P1P2|SNRPD2_+_…
## 13 EIF3D             0.0815           8899. EIF3D_+_36925166.23-P1P2|EIF3D_-_36…
## 14 PRPF40A           0.0818           8242. PRPF40A_+_153573726.23-P1P2|PRPF40A…
## 15 MTOR              0.0819           8818. MTOR_+_11322547.23-P1P2|MTOR_+_1132…
## 16 SRSF7             0.0820           9748. SRSF7_+_38978481.23-P1P2|SRSF7_-_38…
## 17 SRRT              0.0821           6632. SRRT_+_100472814.23-P1P2|SRRT_+_100…
## 18 HSPA9             0.0822           7158. HSPA9_-_137911079.23-P1P2|HSPA9_-_1…
## 19 SLBP              0.0823          10994. SLBP_-_1713995.23-P1P2|SLBP_+_17136…
## 20 CASP8AP2          0.0823          10865. CASP8AP2_-_90539614.23-P1P2|CASP8AP…

Distal

df_targets %>%
    mutate(sgID_AB=fct_reorder(sgID_AB, -pct_distal)) %>%
    ggplot(aes(x=sgID_AB, y=pct_distal, fill=target_gene == "non-targeting")) +
    geom_bar(stat='identity') +
    scale_y_continuous(expand=c(0,0,0.05,0)) +
    scale_fill_manual(values=c('grey', 'red')) +
    theme_bw() +
    theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) + 
    labs(x="Target Gene", y="Percent UMIs Distal Transcripts", fill="Control")

g <- df_targets %>%
    mutate(is_nt=target_gene == "non-targeting") %>%
    ggplot(aes(x="Perturbations", y=pct_distal)) +
    geom_violin(fill='lightgrey', size=0.2, draw_quantiles=c(0.25, 0.50, 0.75)) +
    geom_quasirandom(aes(color=is_nt, size=is_nt, text=target_gene), 
                     pch=16) +
    scale_color_manual(values=c("black", "red")) +
    scale_size_manual(values=c(0.1,1), guide='none') +
    scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
    theme_bw() +
    labs(x=NULL, y="Percent UMIs from distal isoforms",
         color="Control")
## Warning: Ignoring unknown aesthetics: text
ggplotly(g, tooltip=c("text", "y")) %>%
    style(hoverinfo = "skip", traces = 1)
df_targets %>%
    select(target_gene, pct_distal, mean_umis_total, sgID_AB) %>%
    slice_max(pct_distal, n=20)
## # A tibble: 20 × 4
##    target_gene pct_distal mean_umis_total sgID_AB                               
##    <chr>            <dbl>           <dbl> <chr>                                 
##  1 TAF10            0.435           6087. TAF10_-_6633436.23-P1P2|TAF10_+_66334…
##  2 FIP1L1           0.435           6997. FIP1L1_+_54243867.23-P1P2|FIP1L1_-_54…
##  3 TAF2             0.432           6366. TAF2_-_120844853.23-P1P2|TAF2_-_12084…
##  4 TAF8             0.431           6889. TAF8_-_42018330.23-P1P2|TAF8_+_420183…
##  5 TAF12            0.431           6087. TAF12_+_28969568.23-P1P2|TAF12_+_2896…
##  6 PCF11            0.430           8077. PCF11_-_82868415.23-P1P2|PCF11_-_8286…
##  7 TAF1             0.430           6047. TAF1_-_70586444.23-P1P2|TAF1_+_705866…
##  8 TAF7             0.430           7110. TAF7_+_140700244.23-P1P2|TAF7_+_14070…
##  9 SRSF7            0.430           9748. SRSF7_+_38978481.23-P1P2|SRSF7_-_3897…
## 10 CPSF4            0.430           6830. CPSF4_-_99036611.23-P1P2|CPSF4_-_9903…
## 11 TAF6             0.430           7708. TAF6_-_99716931.23-P1P2|TAF6_+_997169…
## 12 NPAT             0.430          11484. NPAT_-_108093360.23-P1P2|NPAT_+_10809…
## 13 SRRT             0.429           6632. SRRT_+_100472814.23-P1P2|SRRT_+_10047…
## 14 UPF1             0.429          10350. UPF1_+_18942771.23-P1P2|UPF1_-_189428…
## 15 CSTF3            0.429           8171. CSTF3_+_33183038.23-P1P2|CSTF3_-_3318…
## 16 CPSF2            0.428           8938. CPSF2_-_92588375.23-P1P2|CPSF2_+_9258…
## 17 MRPS14           0.428           9269. MRPS14_-_174992444.23-P1P2|MRPS14_+_1…
## 18 TAF5             0.428           6165. TAF5_-_105127776.23-P1P2|TAF5_+_10512…
## 19 CASP8AP2         0.428          10865. CASP8AP2_-_90539614.23-P1P2|CASP8AP2_…
## 20 CPSF1            0.428           7674. CPSF1_+_145634685.23-P1P2|CPSF1_-_145…
df_targets %>%
    select(target_gene, pct_distal, mean_umis_total, sgID_AB) %>%
    slice_min(pct_distal, n=20)
## # A tibble: 20 × 4
##    target_gene pct_distal mean_umis_total sgID_AB                               
##    <chr>            <dbl>           <dbl> <chr>                                 
##  1 NUDT21           0.385           9659. NUDT21_+_56485197.23-P1P2|NUDT21_-_56…
##  2 CPSF6            0.394           8542. CPSF6_+_69633592.23-P1P2|CPSF6_+_6963…
##  3 OGFOD1           0.395          10061. OGFOD1_-_56485723.23-P1P2|OGFOD1_+_56…
##  4 GATA1            0.408           3819. GATA1_-_48645022.23-P1P2|GATA1_+_4864…
##  5 NKAP             0.409           9616. NKAP_-_119077682.23-P1P2|NKAP_+_11907…
##  6 PHB              0.410           7139. PHB_+_47492183.23-P1P2|PHB_+_47492231…
##  7 PHB2             0.410           7284. PHB2_+_7079788.23-P1P2|PHB2_-_7079800…
##  8 RFFL             0.410          10038. RFFL_-_33416281.23-P1P2|RFFL_+_334162…
##  9 EIF2S3           0.410           7363. EIF2S3_-_24073117.23-P1P2|EIF2S3_+_24…
## 10 DNAJC17          0.411           9633. DNAJC17_-_41099633.23-P1P2|DNAJC17_+_…
## 11 HSPA9            0.411           7158. HSPA9_-_137911079.23-P1P2|HSPA9_-_137…
## 12 EIF2S1           0.411           7588. EIF2S1_-_67827085.23-P1P2|EIF2S1_-_67…
## 13 DDX6             0.411           8935. DDX6_+_118661671.23-P1P2|DDX6_+_11866…
## 14 CCT2             0.411           9295. CCT2_-_69979656.23-P1P2|CCT2_-_699792…
## 15 CNOT1            0.412          11112. CNOT1_+_58663696.23-P1P2|CNOT1_-_5866…
## 16 POLR3A           0.412           8672. POLR3A_-_79789274.23-P1P2|POLR3A_+_79…
## 17 AARS             0.412           8131. AARS_+_70323362.23-P1P2|AARS_-_703233…
## 18 TIMM44           0.412           9132. TIMM44_-_8008522.23-P1P2|TIMM44_+_800…
## 19 CCT7             0.412           9386. CCT7_-_73461472.23-P1P2|CCT7_-_734614…
## 20 CCT3             0.412          10074. CCT3_+_156308067.23-P1P2|CCT3_-_15630…

Pair Plots

Proximal vs Distal

g <- df_targets %>%
    mutate(is_nt=target_gene == "non-targeting") %>%
    ggplot(aes(x=pct_proximal, y=pct_distal, color=is_nt, text=target_gene)) +
    geom_point(aes(size=n_cells), pch=16) +
    geom_rug(size=0.1) +
    scale_color_manual(values=c("black", "red")) +
    scale_size_continuous(range=c(0.05, 3)) +
    scale_x_continuous(labels=scales::percent_format(accuracy=1)) +
    scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
    theme_bw() +
    theme(legend.position="none") +
    labs(x="Percent UMIs from proximal isoforms",
         y="Percent UMIs from distal isoforms")

ggplotly(g, tooltip=c("text", "x", "y", "n_cells"))

Similar to the first pass, we observed three extreme outliers that have isoform switching effects: CPSF6, NUDT21, and OGFOD1. In the same direction - shortening when knocked down - we see CNOT1, PABPC1, and GATA1, but with milder effect sizes. In the opposite direction - lengthening when knocked-down - we see PCF11, SRSF7, FIP1L1, and NPAT.

Other factors appear to impact the isoforms almost independently. Namely, knockdown of TAF proteins have a strong increase in distal isoforms. Similarly, core factors, such as, CPSF1-4, CSTF1, and CSTF3.

The PAF1 complex members form an outlier set that reduces proximial isoform usage with little impact on distal usage, such as, RTF1, PAF1, and CTF9.

IPA vs Proximal

g <- df_targets %>%
    mutate(is_nt=target_gene == "non-targeting") %>%
    ggplot(aes(x=pct_ipa, y=pct_proximal, color=is_nt, text=target_gene)) +
    geom_point(aes(size=n_cells), pch=16) +
    geom_rug(size=0.1) +
    scale_color_manual(values=c("black", "red")) +
    scale_size_continuous(range=c(0.05, 3)) +
    scale_x_continuous(labels=scales::percent_format(accuracy=1)) +
    scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
    theme_bw() +
    theme(legend.position="none") +
    labs(x="Percent UMIs from IPA isoforms",
         y="Percent UMIs from proximal isoforms")

ggplotly(g, tooltip=c("text", "x", "y", "n_cells"))

Again, the isoform switchers from above are extreme outliers. Here we see that knockdown of PAF1 complex members reduces proximal usage in favor of increased IPA usage. This is in contrast to GATA1 knockdown, which increase both IPA and proximal usage.

IPA vs Distal

g <- df_targets %>%
    mutate(is_nt=target_gene == "non-targeting") %>%
    ggplot(aes(x=pct_ipa, y=pct_distal, color=is_nt, text=target_gene)) +
    geom_point(aes(size=n_cells), pch=16) +
    geom_rug(size=0.1) +
    scale_color_manual(values=c("black", "red")) +
    scale_size_continuous(range=c(0.05, 3)) +
    scale_x_continuous(labels=scales::percent_format(accuracy=1)) +
    scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
    theme_bw() +
    theme(legend.position="none") +
    labs(x="Percent UMIs from IPA isoforms",
         y="Percent UMIs from distal isoforms")

ggplotly(g, tooltip=c("text", "x", "y", "n_cells"))

Here we observe the factors whose knockdown increased distal usage independent of proximal usage do so at the cost of decrease IPA usage. These were the TAF members, CPSF1-4, and CSTF3. Notably, CPSF2 and CPSF3 do not appear to impact IPA.

Conclusion

Considering on multi-UTR genes provides a cleaner signal for effects on different isoforms. We now see many of the known factors stand out, and can identify other possible genes with isoform-specific effects.


Runtime Details

Session Info

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
##  [3] Biobase_2.54.0              GenomicRanges_1.46.0       
##  [5] GenomeInfoDb_1.30.0         IRanges_2.28.0             
##  [7] S4Vectors_0.32.0            BiocGenerics_0.40.0        
##  [9] MatrixGenerics_1.6.0        matrixStats_0.61.0         
## [11] Matrix_1.3-4                ggbeeswarm_0.6.0           
## [13] plotly_4.10.0               cowplot_1.1.1              
## [15] forcats_0.5.1               stringr_1.4.0              
## [17] dplyr_1.0.8                 purrr_0.3.4                
## [19] readr_2.1.1                 tidyr_1.1.4                
## [21] tibble_3.1.7                ggplot2_3.3.5              
## [23] tidyverse_1.3.1             magrittr_2.0.3             
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           fs_1.5.2               lubridate_1.8.0       
##  [4] httr_1.4.2             tools_4.1.1            backports_1.4.0       
##  [7] bslib_0.3.1            utf8_1.2.2             R6_2.5.1              
## [10] vipor_0.4.5            DBI_1.1.1              lazyeval_0.2.2        
## [13] colorspace_2.0-2       withr_2.4.3            tidyselect_1.1.1      
## [16] compiler_4.1.1         cli_3.3.0              rvest_1.0.2           
## [19] xml2_1.3.3             DelayedArray_0.20.0    labeling_0.4.2        
## [22] sass_0.4.0             scales_1.1.1           digest_0.6.29         
## [25] rmarkdown_2.11         XVector_0.34.0         pkgconfig_2.0.3       
## [28] htmltools_0.5.2        highr_0.9              dbplyr_2.1.1          
## [31] fastmap_1.1.0          htmlwidgets_1.5.4      rlang_1.0.2           
## [34] readxl_1.3.1           rstudioapi_0.13        farver_2.1.0          
## [37] jquerylib_0.1.4        generics_0.1.1         jsonlite_1.7.2        
## [40] crosstalk_1.2.0        RCurl_1.98-1.5         GenomeInfoDbData_1.2.7
## [43] Rcpp_1.0.7             munsell_0.5.0          fansi_0.5.0           
## [46] lifecycle_1.0.1        stringi_1.7.6          yaml_2.2.1            
## [49] zlibbioc_1.40.0        grid_4.1.1             crayon_1.4.2          
## [52] lattice_0.20-45        haven_2.4.3            hms_1.1.1             
## [55] knitr_1.39             pillar_1.7.0           reprex_2.0.1          
## [58] glue_1.6.2             evaluate_0.15          data.table_1.14.2     
## [61] modelr_0.1.8           vctrs_0.4.1            tzdb_0.2.0            
## [64] cellranger_1.1.0       gtable_0.3.0           assertthat_0.2.1      
## [67] xfun_0.30              broom_0.8.0            viridisLite_0.4.0     
## [70] beeswarm_0.4.0         ellipsis_0.3.2

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